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检索条件"任意字段=13th IEEE International Symposium on Parallel and Distributed Processing with Applications"

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Multi-level Layout Optimization for Efficient Spatio-temporal Queries on ISABELA-compressed Data

Multi-level Layout Optimization for Efficient Spatio-tempora...

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international symposium on parallel and distributed processing (IPDPS)

作者： Zhenhuan Gong Sriram Lakshminarasimhan John Jenkins Hemanth Kolla Stephane Ethier Jackie Chen Robert Ross Scott Klasky Nagiza F. Samatova Oak Ridge National Laboratory TN USA North Carolina State University NC USA Sandia National Laboratory Livermore CA USA Princeton Plasma Physics Laboratory Princeton NJ USA Argonne National Laboratory Argonne IL USA

ISBN: (纸本)9781467309752

the size and scope of cutting-edge scientific simulations are growing much faster than the I/O subsystems of their runtime environments, not only making I/O the primary bottleneck, but also consuming space that pushes the storage capacities of many computing facilities. these problems are exacerbated by the need to perform data-intensive analytics applications, such as querying the dataset by variable and spatio-temporal constraints, for what current database technologies commonly build query indices of size greater than that of the raw data. To help solve these problems, we present a parallel query-processing engine that can handle both range queries and queries with spatio-temporal constraints, on B-spline compressed data with user-controlled accuracy. Our method adapts to widening gaps between computation and I/O performance by querying on compressed metadata separated into bins by variable values, utilizing Hilbert space-filling curves to optimize for spatial constraints and aggregating data access to improve locality of per-bin stored data, reducing the false positive rate and latency bound I/O operations (such as seek) substantially. We show our method to be efficient with respect to storage, computation, and I/O compared to existing database technologies optimized for query processing on scientific data.

关键词： Layout Query processing Indexes Organizations Splines (mathematics) Computational modeling Bandwidth

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Workshop 13: PDSEC parallel and distributed Scientific and Engineering Computing

Workshop 13: PDSEC Parallel and Distributed Scientific and E...

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ieee international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Raphaël Couturier Peter Strazdins Eric Aubanel Sabine Roller Laurence T. Yang thomas Rauber Gudula Runger University of Franche-Comté France The Australian National University Australia University of New Brunswick Canada University of Siegen Germany St. Francis Xavier University Canada University of Bayreuth Germany Chemnitz University of Technology Germany

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Summary form only given, as follows. the complete presentation was not made available for publication as part of the conference proceedings. the technological trends in HPC system evolution indicates an increasing burden placed on application developers due to the management of the unprecedented complexity levels of hardware and its associated performance characteristics. Many existing scientific applications codes are unlikely to perform well on future systems without major modifications or even complete rewrites. In the future, it will be necessary to utilize, in concert, many characteristics such as multiple levels of parallelism, many lightweight cores, complex memory hierarchies, novel I/O technology, power capping, system-wide temporal/spatial performance heterogeneity and reliability concerns. the parallel and distributed computing (PDC) community has developed new programming models, algorithms, libraries and tools to meet these challenges in order to accommodate productive code development and effective system use. However, the scientific application community still needs to identify the benefit through practical evaluations. thus, the focus of this workshop is on methodologies and experiences used in scientific and engineering applications and algorithms to achieve sustainable code development for better productivity, application performance and reliability.

关键词： Handheld computers Reliability distributed processing Market research

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distributed computing with load-managed active storage

Distributed computing with load-managed active storage

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international symposium on High Performance distributed Computing

作者： R. Wickremesinghe J.S. Chase J.S. Vitter Department of Computer Science Duke University Durham NC USA

One approach to high-performance processing of massive data sets is to incorporate computation into storage systems. Previous work has shown that this active storage model is effective for a variety of problems. this paper explores opportunities to use active storage as a basis for exploiting asymmetric parallelism in applications using a streaming computation model on collections of fixed-size records. this model is the basis for much of the research in I/O-efficient algorithms, which deals with an important class of massive data problems not studied in previous work on active storage. We present an extension of a streaming computation model for an external memory toolkit to support a flexible mapping of computations to storage-based processors. Our approach enables load-managed active storage: it exposes parallelism, ordering constraints, and primitive computation units to the system, which can configure the application to balance load and make the best use of available processing power Emulation results from a sorting application demonstrate the potential of dynamic adaptation in load-managed active storage.

关键词： distributed computing parallel processing Power system modeling Computational modeling Concurrent computing Computer networks Large-scale systems Computer science Emulation Sorting

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Multigrain parallelism for eigenvalue computations on networks of clusters

Multigrain parallelism for eigenvalue computations on networ...

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international symposium on High Performance distributed Computing

作者： J.R. McCombs A. Stathopoulos Department of Computer Science College of William and Mary Williamsburg VA USA

Clusters of workstations have become a cost-effective means of performing scientific computations. However, large network latencies, resource sharing, and heterogeneity found in networks of clusters and Grids can impede the performance of applications not specifically tailored for use in such environments. A typical example is the traditional fine grain implementations of Krylov-like iterative methods, a central component in many scientific applications. To exploit the potential of these environments, advances in networking technology must be complemented by advances in parallel algorithmic design. In this paper, we present an algorithmic technique that increases the granularity of parallel block iterative methods by inducing additional work during the preconditioning (inexact solution) phase of the iteration. During this phase, each vector in the block is preconditioned by a different subgroup of processors, yielding a much coarser granularity. the rest of the method comprises a small portion of the total time and is still implemented in fine grain. We call this combination of fine and coarse grain parallelism multigrain. We apply this idea to the block Jacobi-Davidson eigensolver, and present experimental data that shows the significant reduction of latency effects on networks of clusters of roughly equal capacity and size. We conclude with a discussion on how multigrain can be applied dynamically based on runtime network performance monitoring.

关键词： parallel processing Eigenvalues and eigenfunctions Computer networks Concurrent computing Delay Iterative methods Iterative algorithms Workstations Resource management Impedance

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A dynamic hybrid scheduling algorithm with clients' departure for impatient clients in heterogeneous environments

A dynamic hybrid scheduling algorithm with clients' departur...

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international symposium on parallel and distributed processing (IPDPS)

作者： N. Saxena Kalyan Basu S.K. Das C.M. Pinotti Computer Science & Engineering Department University of Texas Arlington Arlington TX USA Department of Mathematics and Informatics University of Perugia Perugia Italy

the essence of efficient scheduling and data transmission techniques lies in providing the Web-applications with advanced data processing capabilities. In this paper we have efficiently combined the push and the pull scheduling to develop a new, practical, dynamic, hybrid scheduling strategy for heterogenous, asymmetric environments. the proposed algorithm dynamically computes the probabilities and the optimal cutoff-point to separate the push and the pull data sets. the data items are also assumed to be of variable lengths. While the push strategy uses the flat, round-robin scheduling, the pull items are determined by stretch-optimal (max-request min-service time) scheduling policy. In order to make the scheduling more practical, we have considered the impact of the impatience of the clients waiting to get the service of a particular data item. the effects of this impatience can lead to departure of specific client(s) from the system. Our proposed hybrid scheduling strategy takes care of these effects to capture a real portrayal of the system dynamics. these scenarios are modelled by suitable birth and death process to analyze the overall expected delay of the system. Subsequently, simulation results corroborate the average system performance and points out significant improvement over existing hybrid systems in terms of average waiting time spent by a client.

关键词： Scheduling algorithm Processor scheduling Dynamic scheduling Broadcasting Data communication Quality of service Delay Mathematics Informatics Data processing

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PAMI: A parallel Active Message Interface for the Blue Gene/Q Supercomputer

PAMI: A Parallel Active Message Interface for the Blue Gene/...

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international symposium on parallel and distributed processing (IPDPS)

作者： Sameer Kumar Amith R. Mamidala Daniel A. Faraj Brian Smith Michael Blocksome Bob Cernohous Douglas Miller Jeff Parker Joseph Ratterman Philip Heidelberger Dong Chen Burkhard Steinmacher-Burrow IBM T.J. Watson Research Center Yorktown Heights NY USA IBM Systems and Technology Group Rochester MN USA IBM Deutschland GmbH Ehningen Baden-WÃ¼rttemberg DE

ISBN: (纸本)9781467309752

the Blue Gene/Q machine is the next generation in the line of IBM massively parallel supercomputers, designed to scale to 262144 nodes and sixteen million threads. With each BG/Q node having 68 hardware threads, hybrid programming paradigms, which use message passing among nodes and multi-threading within nodes, are ideal and will enable applications to achieve high throughput on BG/Q. With such unprecedented massive parallelism and scale, this paper is a groundbreaking effort to explore the design challenges for designing a communication library that can match and exploit such massive parallelism In particular, we present the parallel Active Messaging Interface (PAMI) library as our BG/Q library solution to the many challenges that come with a machine at such scale. PAMI provides (1) novel techniques to partition the application communication overhead into many contexts that can be accelerated by communication threads, (2) client and context objects to support multiple and different programming paradigms, (3) lockless algorithms to speed up MPI message rate, and (4) novel techniques leveraging the new BG/Q architectural features such as the scalable atomic primitives implemented in the L2 cache, the highly parallel hardware messaging unit that supports both point-to-point and collective operations, and the collective hardware acceleration for operations such as broadcast, reduce, and all reduce. We experimented with PAMI on 2048 BG/Q nodes and the results show high messaging rates as well as low latencies and high throughputs for collective communication operations.

关键词： Message systems Context Hardware Programming Libraries parallel processing Acceleration

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Quantitative Trait Locus Analysis Using a Partitioned Linear Model on a GPU Cluster

Quantitative Trait Locus Analysis Using a Partitioned Linear...

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ieee international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Peter E. Bailey Tapasya Patki Gregory M. Striemer Ali Akoglu David K. Lowenthal Peter Bradbury Matt Vaughn Liya Wang Stephen Goff Department of Computer Science University of Arizona Tucson USA Department of Electrical and Computer Engineering University of Arizona Tucson USA USDA/ARS and Cornell University USA Texas Advanced Computing Center USA Cold Spring Harbor Laboratory USA IPlant Collaborative USA

Quantitative Trait Locus (QTL) analysis is a statistical technique that allows understanding of the relationship between plant genotypes and the resultant continuous phenotypes in non-constant environments. this requires generation and processing of large datasets, which makes analysis challenging and slow. One approach, which is the subject of this paper, is Partitioned Linear Modeling (PLM), lends itself well to parallelization, both by MPI between nodes and by GPU within nodes. Large input datasets make this parallelization on the GPU non-trivial. this paper compares several candidate integrated MPI/GPU parallel implementations of PLM on a cluster of GPUs for varied data sets. We compare them to a naive implementation and show that while that implementation is quite efficient on small data sets, when the data set is large, data-transfer overhead dominates an all-GPU implementation of PLM. We show that an MPI implementation that selectively uses the GPU for a relative minority of the code performs best and results in a 64 improvement over the MPI/CPU version. As a first implementation of PLM on GPUs, our work serves as a reminder that different GPU implementations are needed, depending on the size of the working set, and that data intensive applications are not necessarily trivially parallelizable with GPUs.

关键词： Graphics processing unit Instruction sets Random access memory Kernel Genetics Registers Memory management

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Towards Energy Efficient Allocation for applications in Volunteer Cloud

Towards Energy Efficient Allocation for Applications in Volu...

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ieee international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Congfeng Jiang Jian Wan Christophe Cérin Paolo Gianessi Yanik Ngoko Hangzhou Dianzi University Hangzhou China PRES Sorbonne Paris Cité Université de Paris 13 Villetaneuse France

ISBN: (纸本)9781479941155

We can view the topology of classical clouds infrastructures as data centers to which are connected user machines. In these architectures the computations are centered on a subset of machines (the data centers) among the possible ones. In our study, we propose to consider an alternative view of clouds where both users machines and data centers are used for servicing requests. We refer to these clouds as volunteer clouds. Volunteer clouds offer potential advantages in elasticity and energy savings, but we have also to manage the unavailability of volunteer nodes. In this paper, we are interested in optimizing the energy consumed by the provisioning of applications in volunteer clouds. Given a set of applications requested by cloud's clients for a window of time, the objective is to find the deployment plan that is less energy consuming. In comparison with many works in resource allocation, our specificity is in the management of the unavailability of volunteer nodes. We show that our core challenge can be formalized as an NP-hard and inapproximable problem. We then propose an ILP (Integer Linear Programming) model and various greedy heuristics for its resolution. Finally, we provide an experimental analysis of our proposal in using realistic data and modeling for energy consumption. this work is a work on modeling with simulation results but not a work with emulation and experiments on real systems. However, the parameters and assumptions made for our simulations fit well with the knowledge generally accepted by people working on energy modeling and volunteer computing. Consequently our work should be analyzed as a solid building block towards the implementation of allocation mechanisms in volunteer clouds.

关键词： Resource management Energy consumption Availability Computational modeling Energy resolution Elasticity Integer linear programming

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Policies for swapping MPI processes 03

Policies for swapping MPI processes

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international symposium on High Performance distributed Computing

作者： O. Sievert H. Casanova Department of Computer Science and Engineering University of California San Diego USA University of California San Diego La Jolla CA US Dept. of Comput. Sci. & Eng. California Univ. San Diego CA USA

ISBN: (纸本)9780769519654

Despite the enormous amount of research and development work in the area of parallel computing, it is a common observation that simultaneous performance and ease-of-use are elusive. We believe that ease-of-use is critical for many end users, and thus seek performance enhancing techniques that can be easily retrofitted to existing parallel applications. In a precious paper we have presented MPI (message passing interface) process swapping, a simple add-on to the MPI programming environment that can improve performance in shared computing environments. MPI process swapping requires as few as three lines of source code change to an existing application. In this paper we explore a question that we had left open in our previous work: based on which policies should processes be swapped for best performance? Our results show that, with adequate swapping policies, MPI process swapping can provide substantial performance benefits with very limited implementation effort.

关键词： parallel processing distributed computing parallel programming Message passing Computer science Supercomputers Research and development Programming environments Writing Scalability

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A Predictive Model for Solving Small Linear Algebra Problems in GPU Registers

A Predictive Model for Solving Small Linear Algebra Problems...

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international symposium on parallel and distributed processing (IPDPS)

作者： Michael J. Anderson David Sheffield Kurt Keutzer Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA

We examine the problem of solving many thousands of small dense linear algebra factorizations simultaneously on Graphics processing Units (GPUs). We are interested in problems ranging from several hundred of rows and columns to 4 x 4 matrices. Problems of this size are common, especially in signal processing. However, they have received very little attention from current numerical linear algebra libraries for GPUs, which have thus far focused only on very large problems found in traditional supercomputing applications and benchmarks. To solve small problems efficiently we tailor our implementation to the GPUs inverted memory hierarchy and multi-level parallelism hierarchy. We provide a model of the GPU memory subsystem that can accurately predict and explain the performance of our approach across different problem sizes. As a motivating example, we look at space-time adaptive radar processing, a real-time application that requires hundreds of independent QR factorizations of small complex matrices (e.g. 240 × 66). For realistic matrix sizes from a standard radar processing benchmark, our implementation on an NVIDIA Quadro 6000 GPU runs 2.8 × to 25 × faster than Intel's Math Kernel Library (MKL) on an Intel Core i7-2600. For the QR factorizations of 5,000 56 × 56 single-precision matrices, our approach runs 29 × faster than MKL and 140 × faster than the state-of-the-art linear algebra library for GPUs. In each of these cases we are using the GPU's hardwareaccelerated division and square root functions that are accurate up to 22 mantissa bits.

关键词： Bandwidth Instruction sets Graphics processing unit Registers Mathematical model Linear algebra Benchmark testing

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